Virtual simulation practical training platform of intelligent networked automobile environment sensing system

Abstract

The invention provides a virtual simulation practical training platform of an intelligent networked automobile environment sensing system, which comprises a virtual traffic environment construction module, an environment sensing data simulation module, a test module, a practical training teaching module, a data management module and an optimization module, according to the platform, information and corresponding perception data in a virtual environment can be obtained according to mutual cooperation among the simulated traffic environment construction module, the environment perception data simulation module, the test module, the practical training teaching module, the data management module and the optimization module, and a user can design and test a scene. And the practical training and teaching module can carry out corresponding practical operation and experiment, so that the practical ability and problem solving ability of the user are improved, and the stored data can be collected, stored and analyzed through the data management and analysis module, so that corresponding optimization can be carried out.

Description

Technical Field

[0001] The present invention relates to the field of automobile training technology, and in particular to a virtual simulation training platform for an intelligent networked automobile environment perception system. Background Art

[0002] With the rapid development of intelligent connected vehicle technology, the importance of environmental perception system as the "eyes" and "ears" of autonomous driving technology has become increasingly prominent. The environmental perception system uses multiple sensors such as cameras, radars, and lidars to obtain real-time information about roads, pedestrians, vehicles, traffic signs, etc. around the vehicle, providing key basis for autonomous driving decisions. However, in practical applications, environmental perception systems face many challenges, such as complex and changeable traffic environments, severe weather conditions, errors and noise of the sensors themselves, etc., which may lead to errors in perception results, thereby affecting the safety and reliability of autonomous driving. In order to meet these challenges, traditional methods usually rely on a large number of actual road tests to verify and optimize the performance of environmental perception systems. However, this method has many limitations. First, the cost of actual road testing is high, requiring a lot of manpower, material and financial resources. Second, there are safety hazards in actual road testing. Once an accident occurs, the consequences will be disastrous. In addition, it is difficult for actual road testing to cover all possible traffic scenarios and weather conditions, resulting in limited comprehensiveness and accuracy of test results. Therefore, the virtual simulation training platform for the environmental perception system of intelligent connected vehicles is designed to solve the above problems. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a virtual simulation training platform for an intelligent connected vehicle environment perception system to at least solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The virtual simulation training platform of the intelligent connected vehicle environment perception system includes a virtual traffic environment construction module, an environment perception data simulation module, a test module, a training teaching module, a data management module and an optimization module. The virtual traffic environment construction module is used to simulate the basic environment layer, the traffic participant layer and the environment dynamic change layer. The environment perception data simulation module is used to simulate vehicle sensors. The test module is used to design test vehicle scenes. The training teaching module is used for training teaching. The data management module is used to store and analyze the data in the virtual traffic environment construction module, the environment perception data simulation module, the test module and the training teaching module, and respectively obtain environmental analysis results, perception analysis results, test analysis results and training teaching analysis results. The optimization module gives corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and training teaching optimization suggestions based on the analysis opinions in the environmental analysis results, perception analysis results, test analysis results and training teaching analysis results.

[0006] Furthermore, the simulation basic environment layer is specifically: through virtual reality technology, terrain simulation, road signs and traffic signals are simulated to simulate the real-world traffic conditions; the simulation traffic participant layer is specifically: through virtual reality technology, the driving characteristics of different types of vehicles are simulated to simulate the real-world traffic conditions; the simulation environment dynamic layer is specifically: through simulation reality technology, the time, light, temperature, and humidity parameters in the virtual construction environment are simulated to simulate the traffic environment under different weather conditions.

[0007] Furthermore, simulating vehicle sensors specifically includes: simulating the vehicle's camera, radar, and lidar sensors through virtual reality technology.

[0008] Furthermore, the design of the test car scenario is specifically as follows: through virtual reality technology, the basic environment layer, traffic participant layer, and environmental dynamic change layer are simulated to design the vehicle driving environment test scenario, and the data detected by the vehicle camera, radar, and lidar sensors in the vehicle driving environment test scenario are recorded.

[0009] Furthermore, the practical training teaching module is used for practical training teaching specifically as follows: by designing teaching content, including building a virtual traffic environment, building simulated environmental perception, and designing a vehicle driving environment test scenario, based on the teaching content, students are placed in the vehicle driving environment test scenario through augmented reality AR technology.

[0010] Furthermore, the data management module is used to store and analyze the data in the virtual traffic environment construction module, the environmental perception data simulation module, the test module, and the practical training teaching module, and obtain the environmental analysis results, the perception analysis results, the test analysis results, and the practical training teaching analysis results respectively, specifically:

[0011] The data management module will simulate the terrain, road signs, traffic signals, and traffic participants in the basic environment layer, the driving characteristics data of different types of vehicles in the simulated traffic participant layer, and the time, light, temperature, and humidity in the simulated environment dynamic layer; the data of simulated vehicle cameras, radars, and lidar sensors in the environmental perception data module; the driving data of vehicles in different virtual traffic environments and the data of vehicle cameras, radars, and lidar sensors in the test module; the teaching content data in the practical training teaching module and the test scene data of the vehicle driving environment in which the students are substituted are stored; and the stored data are analyzed according to the environmental simulation, perception simulation, test simulation, and practical training teaching simulation standards to obtain environmental analysis results, perception analysis results, test analysis results, and practical training teaching analysis results, respectively.

[0012] Furthermore, the optimization module gives corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical training optimization suggestions based on the analysis opinions in the environmental analysis results, perception analysis results, test analysis results and practical training analysis results. The specific suggestions are as follows:

[0013] If any of the environmental analysis results, perception analysis results, test analysis results and practical training teaching analysis results does not meet the corresponding environmental simulation, perception simulation, test simulation and practical training teaching simulation standards, then corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical training teaching optimization suggestions will be given respectively.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] The present invention proposes a virtual simulation training platform for an intelligent connected vehicle environment perception system. The platform can obtain information in a virtual environment and corresponding perception data based on the mutual collaboration among a simulated traffic environment construction module, an environment perception data simulation module, a test module, a training and teaching module, a data management module and an optimization module. Users can design tests through scenarios, and the training and teaching modules can perform corresponding actual operations and experiments, thereby improving users' practical ability and problem-solving ability. The platform can also collect, store and analyze stored data through the data management and analysis module in order to perform corresponding optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1It is a schematic diagram of the overall structure of the virtual simulation training platform for the intelligent connected vehicle environment perception system proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0019] Reference Figure 1 The present invention provides a virtual simulation training platform for an intelligent networked vehicle environment perception system, including a virtual traffic environment construction module, an environment perception data simulation module, a test module, a training teaching module, a data management module and an optimization module. The virtual traffic environment construction module is used to simulate a basic environment layer, a traffic participant layer and an environment dynamic change layer. The environment perception data simulation module is used to simulate vehicle sensors. The test module is used to design a test vehicle scene. The training teaching module is used for training teaching. The data management module is used to store and analyze data in the virtual traffic environment construction module, the environment perception data simulation module, the test module and the training teaching module, and respectively obtain environmental analysis results, perception analysis results, test analysis results and training teaching analysis results. The optimization module is used for the environmental analysis results. Based on the analysis opinions in the results, perception analysis results, test analysis results and practical teaching analysis results, corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical teaching optimization suggestions are given respectively. The simulation basic environment layer is specifically: through virtual reality technology, terrain simulation, road signs and traffic signals are simulated to simulate the traffic conditions in the real world; the simulation traffic participant layer is specifically: through virtual reality technology, the driving characteristics of different types of vehicles are simulated to simulate the traffic conditions in the real world; the simulation environment dynamic layer is specifically: through simulation reality technology, the time, light, temperature, and humidity parameters in the virtual construction environment are simulated to simulate the traffic environment under different weather conditions; the simulation vehicle sensor is specifically: through virtual reality technology, the vehicle's camera, radar, and lidar sensors are simulated.

[0020] Exemplarily, the virtual traffic environment construction module is responsible for simulating a real traffic environment, including the basic environment layer: simulating infrastructure such as topography, road signs and traffic signals. For example, it can simulate a complex urban intersection, including multiple lanes, traffic lights, pedestrian crossing facilities, etc.; the traffic participant layer: simulating different types of vehicles and pedestrians, including cars, trucks, motorcycles, bicycles and pedestrians, etc. Traffic participants have different driving characteristics and behavior patterns, such as speed, acceleration, steering, etc.; the environment dynamic change layer simulates the dynamic changes of environmental factors such as time, light, temperature, humidity parameters, for example, it can simulate the slippery road conditions on rainy days, or simulate the lack of light at night; the environmental perception data simulation module simulates the sensor system of the intelligent connected car, including cameras, radars and lidars, etc. These sensors can perceive the environmental information around the vehicle in real time, such as Road signs, traffic signals, the positions of other vehicles and pedestrians, etc.; the test module is used to design various test scenarios to verify the performance and safety of intelligent connected vehicles under different conditions. For example, an emergency braking test scenario can be designed to simulate the situation where an obstacle suddenly appears in front of the vehicle to evaluate the braking performance and obstacle avoidance ability of the vehicle; the practical training module provides practical training content, and uses augmented reality AR technology to put students into a virtual test scenario. Students can perform actual operations and exercises in this environment to improve their driving skills and ability to cope with complex traffic environments; the data management module is responsible for storing and analyzing data from various modules. These data can be classified and analyzed according to standards such as environmental simulation, perception simulation, test simulation, and practical training simulation to generate corresponding analysis results. The optimization module gives corresponding optimization suggestions based on the analysis results generated by the data management module. For example, if the environmental analysis results show that there is a large difference between the simulated traffic environment and the actual road environment, the optimization module will recommend improvements and optimizations to the virtual traffic environment construction module. Similarly, if the perception analysis results show that the sensor's perception accuracy is insufficient, the optimization module will recommend improvements and optimizations to the environmental perception data simulation module.

[0021] The specific design of the test car scenario is: through virtual reality technology, the basic environment layer, traffic participant layer, and environmental dynamic change layer are simulated to design the vehicle driving environment test scenario, and the data detected by the vehicle camera, radar, and lidar sensors in the vehicle driving environment test scenario are recorded.

[0022] For example, through virtual reality technology, the basic environment layer, traffic participant layer, and environment dynamic change layer are simulated to design a vehicle driving environment test scene. In the designed test scene, the camera, radar, and lidar sensors will detect objects and obstacles in the virtual environment in real time and generate corresponding data. Camera data: The camera will capture images in the virtual environment. These images can be used for training and testing image processing algorithms, such as target detection and image recognition. Radar data: The radar will emit electromagnetic waves and receive reflected signals to measure information such as the distance, speed, and direction of objects. These data can be used for training and testing radar signal processing algorithms. Lidar data: The lidar will emit laser beams and receive reflected signals to generate point cloud data. These data can be used for tasks such as three-dimensional reconstruction and obstacle detection. During the test, the data detected by these sensors can also be recorded for subsequent analysis and processing. Example scenario: Suppose we designed a highway test scene that contains multiple lanes, traffic lights, different types of vehicles, and pedestrians. In this scenario, we set up different weather conditions (such as sunny and rainy days) and time changes (such as day and night), and the test vehicle was equipped with cameras, radars, and lidar sensors. During the test, these sensors detect objects and obstacles in the virtual environment in real time and generate corresponding data. We recorded this data and analyzed the vehicle's perception ability and autonomous driving performance under different weather conditions and time changes.

[0023] The practical training teaching module is used for practical training teaching in the following ways: by designing teaching content, including building a virtual traffic environment, building simulated environmental perception, and designing vehicle driving environment test scenarios, students are placed in the vehicle driving environment test scenarios based on the teaching content through augmented reality AR technology.

[0024] Exemplarily, the practical training module can be used to train trainees to master specific skills or knowledge through the design of teaching content. When designing the practical training content, a virtual traffic environment test scene can be constructed. Using virtual reality technology, a virtual traffic environment containing different road types (such as highways, urban roads, rural roads), traffic signals (such as traffic lights, traffic signs), terrain (such as mountain roads, bridges, tunnels) and other elements can be constructed. A simulated environmental perception test scene can also be constructed. In a virtual environment, the perception capabilities of various sensors (such as cameras, radars, laser radars, etc.) of the vehicle are simulated. These sensors will capture information in the virtual environment in real time and generate corresponding data for subsequent vehicle control and decision-making. Vehicle driving environment test scenes can also be designed. These scenes can simulate different traffic conditions (such as congestion, accidents, emergency braking, etc.), as well as different weather conditions (such as sunny days, rainy days, foggy days, etc.). In addition, augmented reality (AR) technology can be used for practical training. After building rich teaching content, we can use augmented reality (AR) technology to put students into the vehicle driving environment test scene, that is, by wearing AR glasses or helmets and other equipment, students can see virtual objects and information in the real environment. In practical training, AR technology can combine the virtual traffic environment and vehicle driving scene with the real teaching environment to provide students with a more intuitive and immersive learning experience. Students can wear AR glasses to see the virtual traffic environment and vehicle driving scene in the real teaching venue. Students can observe the driving status of virtual vehicles in various scenes in real time, as well as the real-time changes in sensor data. Students can also interact with the virtual environment, such as simulating driving operations, adjusting vehicle parameters, etc., to gain a deeper understanding of the working principles of vehicle perception systems and autonomous driving algorithms.

[0025] The data management module is used to store and analyze the data in the virtual traffic environment construction module, the environmental perception data simulation module, the test module, and the practical training teaching module, and obtain the environmental analysis results, perception analysis results, test analysis results, and practical training teaching analysis results respectively. Specifically:

[0026] The data management module will simulate the terrain, road signs, traffic signals, and traffic participants in the basic environment layer, the driving characteristics data of different types of vehicles in the simulated traffic participant layer, and the time, light, temperature, and humidity in the simulated environment dynamic layer; the data of simulated vehicle cameras, radars, and lidar sensors in the environmental perception data module; the driving data of vehicles in different virtual traffic environments and the data of vehicle cameras, radars, and lidar sensors in the test module; the teaching content data in the practical training teaching module and the test scene data of the vehicle driving environment in which the students are substituted are stored; and the stored data are analyzed according to the environmental simulation, perception simulation, test simulation, and practical training teaching simulation standards to obtain environmental analysis results, perception analysis results, test analysis results, and practical training teaching analysis results, respectively.

[0027] Exemplarily, the data management module stores data from various modules, including but not limited to:

[0028] Virtual traffic environment construction module: Simulation basic environment layer: simulation data of terrain (such as mountains, rivers, buildings, etc.), simulation data of road signs (such as lane lines, traffic signs, etc.), simulation data of traffic signals (such as traffic lights, traffic signs, etc.), simulation data of traffic participants (such as vehicle type, speed, driving trajectory, etc.). Simulation traffic participant layer: driving characteristic data of different types of vehicles (such as acceleration, braking distance, turning radius, etc.). Simulation environment dynamic layer: simulation data of time, light, temperature, and humidity, used to simulate the traffic environment under different weather and time conditions. Environmental perception data simulation module: simulated vehicle camera, radar, lidar sensor and other data, these data are used to simulate the vehicle's perception of the environment during actual driving. Test module: vehicle driving data in different virtual traffic environments, including speed, acceleration, steering angle, etc. Real-time data of vehicle cameras, radars, and lidar sensors during testing. Practical training teaching module: teaching content data involved. Data that students substitute into the vehicle driving environment test scene, including students' operation records, reaction time, etc. After storing the data, the data management module will analyze it according to different simulation standards to obtain various analysis results. Environmental analysis results: By analyzing the data of the simulated basic environment layer, simulated traffic participant layer and simulated environment dynamic layer, the realism and credibility of the virtual traffic environment are evaluated. Check whether the terrain, road signs, traffic signals, etc. accurately simulate the real-world traffic environment. Perception analysis results: Analyze the data of the simulated vehicle camera, radar, and lidar sensors in the environmental perception data simulation module to evaluate the accuracy and reliability of the vehicle perception system. Check the perception ability of the sensor under different environmental conditions, such as night and rainy days. Test analysis results: Analyze the driving data of the vehicle in different virtual traffic environments in the test module to evaluate the driving performance of the vehicle and the effectiveness of the autonomous driving algorithm. Check the response speed and decision-making ability of the vehicle in different scenarios. Practical training analysis results: Analyze the teaching content data involved in the practical training module and the data that the trainees substitute into the vehicle driving environment test scene to evaluate the teaching effect and the learning outcomes of the trainees. Check whether the teaching content is easy to understand and whether the trainees can effectively master key skills and knowledge in the simulation environment.

[0029] The optimization module provides corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions, and practical training optimization suggestions based on the analysis opinions of the environmental analysis results, perception analysis results, test analysis results, and practical training analysis results. The specific suggestions are as follows:

[0030] If any of the environmental analysis results, perception analysis results, test analysis results and practical training teaching analysis results does not meet the corresponding environmental simulation, perception simulation, test simulation and practical training teaching simulation standards, then corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical training teaching optimization suggestions will be given respectively.

[0031] For example, if the environmental analysis results show that the virtual traffic environment is significantly different from the real world in some aspects (such as topography, road signs, traffic signals, etc.), or the simulated behavior of traffic participants does not conform to actual traffic rules, then the optimization module gives environmental optimization suggestions for these problems, such as adjusting the simulation parameters of topography, updating the data of road signs and traffic signals, improving the behavior model of traffic participants, etc., so as to improve the fidelity and credibility of the virtual traffic environment;

[0032] If the perception analysis results show that the vehicle perception system's perception ability is reduced in certain environments (such as at night, on rainy days, etc.), or there are errors in the sensor data, the optimization module will give perception optimization suggestions for these problems, such as improving the sensor design, increasing the sensor sensitivity, optimizing the perception algorithm, etc., to enhance the accuracy and reliability of the vehicle perception system;

[0033] If the test analysis results show that the vehicle's driving performance is poor in certain test scenarios (such as complex intersections, high-speed driving, etc.), or the autonomous driving algorithm has defects, the optimization module will give test optimization suggestions for these problems, such as adjusting the test scenario settings, increasing the diversity of test scenarios, optimizing the autonomous driving algorithm, etc., to improve the vehicle's driving performance and the effectiveness of the autonomous driving algorithm;

[0034] If the practical training analysis results show that students have not mastered certain teaching contents firmly enough, or the practical training effect is not good, the optimization module will give practical training optimization suggestions for these problems, such as improving the teaching content to improve students' learning efficiency and interest.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The virtual simulation training platform for the intelligent connected vehicle environment perception system is characterized by: It includes a virtual traffic environment construction module, an environmental perception data simulation module, a test module, a practical training teaching module, a data management module and an optimization module. The virtual traffic environment construction module is used to simulate the basic environment layer, the traffic participant layer and the environment dynamic change layer. The environmental perception data simulation module is used to simulate vehicle sensors. The test module is used to design and test car scenes. The practical training teaching module is used for practical training teaching. The data management module is used to store and analyze the data in the virtual traffic environment construction module, the environmental perception data simulation module, the test module and the practical training teaching module, and respectively obtain environmental analysis results, perception analysis results, test analysis results and practical training teaching analysis results. The optimization module gives corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical training teaching optimization suggestions based on the analysis opinions in the environmental analysis results, perception analysis results, test analysis results and practical training teaching analysis results.

2. The virtual simulation training platform for the intelligent connected vehicle environment perception system according to claim 1 is characterized in that: The simulation basic environment layer is specifically: through virtual reality technology, terrain simulation, road signs and traffic signals are simulated to simulate the real-world traffic conditions; the simulation traffic participant layer is specifically: through virtual reality technology, the driving characteristics of different types of vehicles are simulated to simulate the real-world traffic conditions; the simulation environment dynamic layer is specifically: through simulation reality technology, the time, light, temperature, and humidity parameters in the virtual construction environment are simulated to simulate the traffic environment under different weather conditions.

3. The virtual simulation training platform for the intelligent connected vehicle environment perception system according to claim 2 is characterized in that: The simulation of vehicle sensors is specifically: using virtual reality technology to simulate the vehicle's camera, radar, and lidar sensors.

4. The virtual simulation training platform for the intelligent connected vehicle environment perception system according to claim 3 is characterized in that: The specific design of the test car scenario is: through virtual reality technology, the basic environment layer, traffic participant layer, and environmental dynamic change layer are simulated to design the vehicle driving environment test scenario, and the data detected by the vehicle camera, radar, and lidar sensors in the vehicle driving environment test scenario are recorded.

5. The virtual simulation training platform for the intelligent connected vehicle environment perception system according to claim 4 is characterized in that: The practical training teaching module is used for practical training teaching in the following ways: by designing teaching content, including building a virtual traffic environment, building simulated environmental perception, and designing vehicle driving environment test scenarios, students are placed in the vehicle driving environment test scenarios based on the teaching content through augmented reality AR technology.

6. The virtual simulation training platform for the intelligent connected vehicle environment perception system according to claim 5 is characterized in that: The data management module is used to store and analyze the data in the virtual traffic environment construction module, the environmental perception data simulation module, the test module, and the practical training teaching module, and obtain the environmental analysis results, perception analysis results, test analysis results, and practical training teaching analysis results respectively. Specifically: The data management module will simulate the terrain, road signs, traffic signals, and traffic participants in the basic environment layer, the driving characteristics data of different types of vehicles in the simulated traffic participant layer, and the time, light, temperature, and humidity in the simulated environment dynamic layer; the data of simulated vehicle cameras, radars, and lidar sensors in the environmental perception data module; the driving data of vehicles in different virtual traffic environments and the data of vehicle cameras, radars, and lidar sensors in the test module; the teaching content data in the practical training teaching module and the test scene data of the vehicle driving environment in which the students are substituted are stored; and the stored data are analyzed according to the environmental simulation, perception simulation, test simulation, and practical training teaching simulation standards to obtain environmental analysis results, perception analysis results, test analysis results, and practical training teaching analysis results, respectively.

7. The virtual simulation training platform for the intelligent connected vehicle environment perception system according to claim 6 is characterized in that: The optimization module gives corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical training optimization suggestions based on the analysis opinions of the environmental analysis results, perception analysis results, test analysis results and practical training analysis results. The specific suggestions are as follows: If any of the environmental analysis results, perception analysis results, test analysis results and practical training teaching analysis results does not meet the corresponding environmental simulation, perception simulation, test simulation and practical training teaching simulation standards, then corresponding environmental optimization suggestions, perception optimization suggestions, test optimization suggestions and practical training teaching optimization suggestions will be given respectively.

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